Electric Drivetrain Optimization for a Commercial Fleet with Different Degrees of Electrical Machine Commonality

Abstract

At present, the prevalence of electric vehicles is increasing continuously. In particular, there are promising applications for commercial vehicles transfering from conventional to full electric, due to lower operating costs and stricter emission regulations. Thus, cost analysis from the fleet perspective becomes important. The study of cost competitiveness of different drivetrain designs is necessary to evaluate the fleet cost variance for different degrees of electrical machine commonality. This paper presents a methodology to find a preliminary powertrain design that minimizes the Total Cost of Ownership (TCO) for an entire fleet of electric commercial vehicles while fulfilling the performance requirements of each vehicle type. This methodology is based on scalable electric machine models, and particle swarm is used as the main optimization algorithm. The results show that the total cost penalty incurred when sharing the same electrical machine is small, therefore, there is a cost saving potential in higher degrees of electrical machine commonality.

Optimizing Parameters for an Electrical Car Employing Vehicle Dynamics Simulation Program

“…In reference [46], in another application field, a particle swarm algorithm is applied for the optimization of vehicle fleets. In [47], MATLAB/Simulink simulations are applied to examine various powertrain configurations and components for designing a hybrid powertrain.…”